Encoder-decoder-based image transformation approach for integrating precipitation forecasts

Hirotaka Hachiya, Yusuke Masumoto, Yuki Mori, Naonori Ueda
Proceedings of The 13th Asian Conference on Machine Learning, PMLR 157:174-188, 2021.

Abstract

As the damage caused by heavy rainfall is becoming more serious, the improvement of precipitation forecasts is highly demanded. For this purpose, arithmetic and Bayesian average-based methods have been proposed to integrate multiple 2D-grid forecasts. However, since a single weight is shared in the entire grid in these methods, local variations of the importance of forecasts could not be taken into account. Besides, although a variety of information is available in precipitation forecast, it would not be straightforwardly to incorporate the additional information in the existing methods. To overcome these problems, we propose an encoder-decoder-based image transformation method that generates a weight image that is optimized in a pixel-wise manner and additional information could be embedded as the channel of input images and feature maps. Through the experiment of precipitation forecast in the period from April 2018 to March 2019 in Japan, we will show that our proposed integration method outperforms existing methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v157-hachiya21a, title = {Encoder-decoder-based image transformation approach for integrating precipitation forecasts}, author = {Hachiya, Hirotaka and Masumoto, Yusuke and Mori, Yuki and Ueda, Naonori}, booktitle = {Proceedings of The 13th Asian Conference on Machine Learning}, pages = {174--188}, year = {2021}, editor = {Balasubramanian, Vineeth N. and Tsang, Ivor}, volume = {157}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v157/hachiya21a/hachiya21a.pdf}, url = {https://proceedings.mlr.press/v157/hachiya21a.html}, abstract = {As the damage caused by heavy rainfall is becoming more serious, the improvement of precipitation forecasts is highly demanded. For this purpose, arithmetic and Bayesian average-based methods have been proposed to integrate multiple 2D-grid forecasts. However, since a single weight is shared in the entire grid in these methods, local variations of the importance of forecasts could not be taken into account. Besides, although a variety of information is available in precipitation forecast, it would not be straightforwardly to incorporate the additional information in the existing methods. To overcome these problems, we propose an encoder-decoder-based image transformation method that generates a weight image that is optimized in a pixel-wise manner and additional information could be embedded as the channel of input images and feature maps. Through the experiment of precipitation forecast in the period from April 2018 to March 2019 in Japan, we will show that our proposed integration method outperforms existing methods.} }
Endnote
%0 Conference Paper %T Encoder-decoder-based image transformation approach for integrating precipitation forecasts %A Hirotaka Hachiya %A Yusuke Masumoto %A Yuki Mori %A Naonori Ueda %B Proceedings of The 13th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Vineeth N. Balasubramanian %E Ivor Tsang %F pmlr-v157-hachiya21a %I PMLR %P 174--188 %U https://proceedings.mlr.press/v157/hachiya21a.html %V 157 %X As the damage caused by heavy rainfall is becoming more serious, the improvement of precipitation forecasts is highly demanded. For this purpose, arithmetic and Bayesian average-based methods have been proposed to integrate multiple 2D-grid forecasts. However, since a single weight is shared in the entire grid in these methods, local variations of the importance of forecasts could not be taken into account. Besides, although a variety of information is available in precipitation forecast, it would not be straightforwardly to incorporate the additional information in the existing methods. To overcome these problems, we propose an encoder-decoder-based image transformation method that generates a weight image that is optimized in a pixel-wise manner and additional information could be embedded as the channel of input images and feature maps. Through the experiment of precipitation forecast in the period from April 2018 to March 2019 in Japan, we will show that our proposed integration method outperforms existing methods.
APA
Hachiya, H., Masumoto, Y., Mori, Y. & Ueda, N.. (2021). Encoder-decoder-based image transformation approach for integrating precipitation forecasts. Proceedings of The 13th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 157:174-188 Available from https://proceedings.mlr.press/v157/hachiya21a.html.

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